Seventeen practices.
One way of working.
Every engagement below runs through the same four phases - diagnose, prove, integrate, operate - so a chat agent and a demand model are held to the same standard of evidence. Start with whichever hurts most.
- 01 AI Customer Support Agents Most support bots are a filter your customers learn to defeat.
- 02 AI Email Automation Batch sends and five static templates leave most of the value on the table.
- 03 Lead Generation & Management Most pipeline is not lost to competitors.
- 04 Business Process Automation The expensive work is rarely inside a system.
- 05 AI for E-Commerce Online retail runs on decisions made thousands of times a day: what to show this visitor, what to reorder, what to price, what to flag.
- 06 Custom AI Development Some problems are specific to you: your terminology, your rules, your data, your edge cases.
- 07 AI Consulting & Strategy The costly mistake is not picking the wrong model.
- 08 Maintenance & Support An AI system does not fail like a server.
- 09 AI Search Visibility (GEO) A growing share of buying research never reaches a results page.
- 10 AI Training & Enablement Most AI training is a vendor demo with a certificate attached.
- 11 Fractional AI Leadership Plenty of companies need someone senior owning AI decisions and nowhere near enough work to justify a full-time hire at that level.
- 12 AI System Rescue The person who built it has left.
- 13 Enterprise Knowledge Assistant Your company already knows the answer.
- 14 Document Processing (IDP) Invoices, purchase orders, claims forms, onboarding packs, contracts.
- 15 AI Cost & Performance Audit Inference costs tend to grow faster than usage, and the bill rarely says why.
- 16 n8n & Make Automation Most n8n and Make work is template configuration that breaks the first time an API changes shape.
- 17 AI Evaluation & QA "It seems good" is the only quality signal most teams have.
Start small
One workflow in a week
Fixed scope, fixed price, five working days
Pick the one repetitive job that costs you the most time. We scope it on a thirty-minute call, build it, and it is running in your systems within five working days of kickoff. If it is not running by day five, there is no invoice.
Delivery model
The same four phases, every time
Sequencing is what stops AI work becoming an open-ended research budget. You get a decision point at the end of each phase - including the option to stop.
- 01
Diagnose
Days 1-2
We sit with the people doing the work and map where time actually goes - queue by queue, handoff by handoff. You get a ranked list of automatable workflows with an effort and payback estimate against each one.
Opportunity map + costed roadmap
- 02
Prove
Week 1
We build the highest-value workflow first and run it against your real historical data, offline. You see accuracy, escalation rate and cost per task before anything touches a live customer.
Working prototype + evaluation report
- 03
Integrate
Weeks 2-3
The system connects to your CRM, helpdesk, store and data warehouse through their supported APIs. Permissions, audit logging, PII handling and human-escalation paths are built in, not bolted on.
Production deployment + runbook
- 04
Operate
Ongoing
Models drift and your business changes. We monitor quality continuously, re-run evaluations on every change, and tune against the metrics you actually care about - not benchmark scores.
Monitoring, evals, quarterly review
FAQ
Questions we get asked first
Something not covered here? Ask directly - we answer questions before contracts.
Ask usHow long before we see something working?
A single well-scoped workflow typically runs in production within three weeks, with a testable prototype inside the first. Broader programmes are sequenced so something ships every few weeks rather than in one large release.
Do we need clean data or a data team first?
No. Most of our engagements begin with the data as it actually is - messy exports, half-filled CRM fields, PDFs. Part of the diagnose phase is establishing what is usable today and what genuinely needs fixing first, rather than a year-long data project ahead of any value.
Which models do you use?
We stay model-agnostic and select per workload against cost, latency and accuracy - frontier hosted models where reasoning quality matters, smaller or open-weight models where volume and privacy dominate. The architecture keeps that choice swappable, so a better or cheaper model later is a configuration change.
What happens to our team?
In practice, the routine tier of work shrinks and the complex tier grows. We plan the role changes with you during the diagnose phase and include enablement so your team can adjust prompts, review escalations and read the dashboards without us.
How do you handle accuracy and hallucination?
Systems are grounded in your own content with retrieval, constrained to the actions they are permitted to take, and measured against a held-out evaluation set that we build from your real cases. Confidence thresholds route uncertain cases to a human, and every change is re-evaluated before it ships.
What does it cost?
The diagnose phase is a fixed fee and delivers a costed roadmap you own regardless of whether you continue. Build work is quoted per workflow, and ongoing support is a monthly plan sized to the number of systems in production. We will give you a range on the first call.
Let's talk
Not sure which one you need?
Most clients do not arrive with a service in mind - they arrive with a bottleneck. Describe it and we will tell you which of these actually applies, or that none of them do.